System and method for improved real-time cine imaging

Abstract

A cine imaging filter and method of use that includes a denoising image-filter based on the Karhunen-Loeve transform along the temporal direction to take advantage of the high temporal correlation among images. The cine imaging filter may further include the application of a simple formula describing the quantitative noise reduction capabilities of the KLT filter as a function of eigenimage cutoff. Additionally, the filter may validate its accuracy in numerical simulation and in in-vivo real time cine images. Furthermore, exemplary embodiments of the cine imaging filter may employ a technique to automatically select the optimal eigenimage cutoff to maximize noise reduction with minimal effect on image information.

1. A computerized method for filtering dynamic images, the method comprising:
 (a) receiving at a computer image data for a set of c dynamic images, each of said images comprising pixel values for an m×n number of pixels; 
 (b) constructing at said computer a matrix (A) comprising a plurality of rows of image data wherein each row comprises said m×n pixels values from each image; 
 (c) determining at said computer within said matrix (A) eigenvalues and eigenvectors of a c by c matrix (AA T ); 
 (d) constructing at said computer one eigenimage using one eigenvector as weight to combine said c original images; 
 (e) setting at said computer an eigenvalue threshold value; 
 (f) removing from said image data all said eigenimages with values below said eigenvalue threshold value; 
 (g) reconstructing at said computer said images using the remaining eigenimages; and 
 (h) providing a computer user with access to said images to facilitate diagnosis of a health condition. 
 
     
     
       2. The method of  claim 1 , wherein said image data comprises dynamic images selected from the group consisting of short-axis views, horizontal long-axis views, and vertical axis views. 
     
     
       3. The method of  claim 1 , wherein the dynamic images are acquired using a real-time steady-state free precession cine sequence combined with the TSENSE with acceleration factor of 4. 
     
     
       4. The method of  claim 1 , wherein said removing from said image data said eigenimages comprises removing eigenimages with autocorrelation full width at half maximum less than or equal to 2.0 pixels. 
     
     
       5. A computerized system for filtering dynamic images, comprising:
 a dynamic image acquiring device for acquiring at least one cine, comprising:
 a scanner; and 
 a phased array coil in communication with the scanner; 
 
 a database of the computer; 
 a computer software program is communication with the database that: 
 (a) receives at a computer image data for a set of c dynamic images, each of said images comprising pixel values for an m×n number of pixels; 
 (b) constructs at said computer a matrix (A) comprising a plurality of rows of image data wherein each row comprises said m×n pixels values from each image; 
 (c) determines at said computer within said matrix (A) eigenvalues and eigenvectors of a c by c matrix (AA T ); 
 (d) constructs at said computer one eigenimage using one eigenvector as weight to combine said c original images; 
 (e) sets at said computer an eigenvalue threshold value; 
 (f) removes from said image data all said eigenimages with values below said eigenvalue threshold value; 
 (g) reconstructs at said computer said images using the remaining eigenimages; and 
 (h) display at said computer said image facilitate diagnosis of a health condition. 
 
     
     
       6. The system of  claim 5 , wherein the dynamic image acquiring device is a clinical MRI system. 
     
     
       7. The system of  claim 5 , wherein the dynamic image acquiring device includes two phased array coils.